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Rehabilitation Dataset Directory: Dataset Profile

Dataset: Inpatient Rehabilitation Facilities Patient Assessment Instrument (IRF-PAI)

Basic Information
Dataset Full Name Inpatient Rehabilitation Facilities Patient Assessment Instrument
Dataset Acronym IRF-PAI
Summary The IRF-PIA contains information on individuals discharged from inpatient rehabilitation facilities (IRF) who are Medicare beneficiaries. These files exist from the year 2002 onwards and include detailed information regarding functional status at the time of admission and discharge from inpatient rehabilitation facilities. The files also contain information related to the quantity of rehabilitation services provided.
Key Terms Functional Status, Rehabilitation Outcomes, Quality Indicators
Study Design Longitudinal
Data Type(s) Administrative
Sponsoring Agency/Entity

Department of Health and Human Services (HHS):

Centers for Medicare and Medicaid Services (CMS)

Health Conditions/Disability Measures
Health Condition(s)

ICD-9/10 diagnostic codes, Body mass index (BMI)/obesity, Diabetes,

Also includes: Orthopedic conditions, Spinal cord injury (SCI), Stroke, Traumatic brain injury (TBI), Cardiovascular conditions, Arthritis, Heart attack, Pressure ulcers

Disability Measures

Ambulatory disability (detailed assessment), Cognitive disability (functional cognition), Communication impairment, Functional limitations (ADLs and/or IADLs) by Functional Independence Measure level (FIM™) of self care performance/ability: ranging from independent (no assistance required)  to dependent (helper does ALL of effort) at admission and discharge. Hearing disability, Self-care disability, Special equipment use/assistive technology,

Measures/Outcomes of Interest
Topics Rehabilitation Outcomes, Functional status, Activities of Daily Living, Mobility, Cognitive status, Case mix groups, Comorbidities, IRF quality indicators, Functional Independence Measure (FIM™), Amount of therapy provided - total minutes/week of: physical therapy, occupational therapy, speech-language pathology
Sample Population Medicare beneficiaries (discharged from IRF)
Sample Size/Notes 400,000(±) Medicare patients qualified to receive post-acute care at IRF
Unit of Observation Patient
Continent(s) North America

United States

Geographic Coverage National
Geographic Specificity Zip Code (of beneficiary’s mailing address)
Special Population(s)

Medicare/Medicaid, Aging/Older people (primarily) 

Data Collection
Data Collection Mode Administrative
Years Collected 2002 - present
Data Collection Frequency Annual
Strengths and Limitations
Strengths Provides a large sample size. Includes longitudinal functional status data and quality indicators. Can be linked with claims data.
Limitations No rehabilitation intervention details. Lacks other clinical information/observation details.
Data Details
Primary Website

Main IRF page:




Data Access https://www.resdac.org/cms-data/files/irf-pai
Data Access Requirements Data Use agreement, $ Cost
Summary Tables/Reports NA
Data Components NA
Selected Papers
Other Papers

Inpatient Rehabilitation Facility Patient Assessment Instrument: 


Variable List:


Related Repositories

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The Rehabilitation Research Cross-dataset Variable Catalog has been developed through the Center for Large Data Research & Data Sharing in Rehabilitation (CLDR). The Center for Large Data Research and Data Sharing in Rehabilitation involves a consortium of investigators from the University of Texas Medical Branch, Cornell University's Yang Tan Institute (YTI), and the University of Michigan. The CLDR is funded by NIH - National Institute of Child Health and Human Development, through the National Center for Medical Rehabilitation Research, the National Institute for Neurological Disorders and Stroke, and the National Institute of Biomedical Imaging and Bioengineering. (P2CHD065702).

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Acknowledgements: This tool was developed through the efforts of William Erickson and Arun Karpur, and web designers Jason Criss and Jeff Trondsen at Cornell University. Many thanks to graduate students Kyoung Jo Oh and Yeong Joon Yoon who developed much of the content used in this tool.

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